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Data Analytics, Artificial Intelligence and Decision Sciences

Neural Networks and Applications in Natural Language Processing

5 units

Please select a city/session before registration.

About this program

Natural Language Processing (NLP) is revolutionizing the way machines interpret human language, powering technologies from chatbots to translation tools. This training course on Neural Networks and Natural Language Processing introduces participants to the core principles of deep learning architectures and their use in NLP applications.
Attendees will delve into text preprocessing techniques, embeddings, recurrent neural networks, and transformer models, alongside exploring practical NLP applications. Through hands-on exercises and case studies, participants will gain experience in constructing language models and deploying them for tasks such as sentiment analysis, text classification, and conversational AI.
By course completion, learners will be equipped to design, train, and assess neural network models tailored for NLP tasks within business and research environments.

Course benefits

  • Gain a foundational understanding of neural networks for NLP
  • Learn to perform text preprocessing and feature engineering
  • Develop and assess NLP models for practical applications
  • Explore advanced transformer architectures including BERT and GPT
  • Enhance capabilities for business, research, and AI-driven systems

Key outcomes

  • Examine neural network structures suited for NLP tasks
  • Implement text cleaning, tokenization, and embedding methods
  • Construct NLP models using RNN, LSTM, and transformer architectures
  • Assess model performance employing NLP-specific metrics and benchmarks
  • Deploy NLP solutions targeting sentiment analysis and text classification
  • Address ethical considerations, bias, and fairness challenges in NLP
  • Incorporate NLP models into operational business processes

Who should attend

  • Data scientists and AI engineers
  • NLP researchers and developers
  • Professionals in business and technology leveraging language AI
  • Analysts aiming to enhance expertise in text analytics

Course outline

1

Unit 1: Foundations of Neural Networks and Natural Language Processing

  • Basic principles of neural networks and deep learning
  • Applications of NLP in business and technology sectors
  • Development history of language processing models
  • Real-world examples demonstrating NLP functionality
2

Unit 2: Text Data Preparation and Feature Extraction

  • Processes of tokenization, stemming, and lemmatization
  • Techniques for vectorizing text (Bag of Words, TF-IDF)
  • Utilization of word embeddings (Word2Vec, GloVe, FastText)
  • Practical exercises on text data preprocessing
3

Unit 3: Applying Neural Networks to NLP Tasks

  • Utilization of Recurrent Neural Networks (RNNs) and LSTMs
  • Applying Convolutional Neural Networks (CNNs) to textual data
  • Understanding attention mechanisms and sequence-to-sequence architectures
  • Interactive model development sessions
4

Unit 4: Transformer Architectures and Advanced Natural Language Processing

  • Overview of transformer models (BERT, GPT, etc.)
  • Techniques for fine-tuning pretrained models on NLP tasks
  • Use cases in machine translation, text summarization, and conversational agents
  • In-depth case studies on sophisticated NLP implementations
5

Unit 5: Ethical Considerations, Performance Assessment, and Business Deployment

  • Addressing bias and ensuring fairness in language models
  • Evaluation metrics for NLP system performance
  • Implementing NLP solutions within organizational environments
  • Emerging directions in neural networks and NLP technologies